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Optimal Operation for Reduced Energy Consumption of an Air Conditioning System Using Neural Inverse Optimal Control

Author

Listed:
  • Flavio Muñoz

    (Tecnologico Nacional de Mexico/ITS de Cajeme, Carretera Internacional a Nogales km 2, Cd. Obregon 85024, Sonora, Mexico)

  • Ramon Garcia-Hernandez

    (Tecnologico Nacional de Mexico/Instituto Tecnologico de La Laguna, Blvd. Revolucion y Av. Instituto Tecnologico de La Laguna S/N, Col. Centro, Torreon 27000, Coahuila, Mexico)

  • Jose Ruelas

    (Tecnologico Nacional de Mexico/ITS de Cajeme, Carretera Internacional a Nogales km 2, Cd. Obregon 85024, Sonora, Mexico)

  • Juan E. Palomares-Ruiz

    (Tecnologico Nacional de Mexico/ITS de Cajeme, Carretera Internacional a Nogales km 2, Cd. Obregon 85024, Sonora, Mexico)

  • Carlos Álvarez-Macías

    (Tecnologico Nacional de Mexico/Instituto Tecnologico de La Laguna, Blvd. Revolucion y Av. Instituto Tecnologico de La Laguna S/N, Col. Centro, Torreon 27000, Coahuila, Mexico)

Abstract

For a comfortable thermal environment, the main parameters are indoor air humidity and temperature. These parameters are strongly coupled, causing the need to search for multivariable control alternatives that allow efficient results. Therefore, in order to control both the indoor air humidity and temperature for direct expansion (DX) air conditioning (A/C) systems, different controllers have been designed. In this paper, a discrete-time neural inverse optimal control scheme for trajectories tracking and reduced energy consumption of a DX A/C system is presented. The dynamic model of the plant is approximated by a recurrent high-order neural network (RHONN) identifier. Using this model, a discrete-time neural inverse optimal controller is designed. Unscented Kalman filter (UKF) is used online for the neural network learning. Via simulation the scheme is tested. The proposed approach effectiveness is illustrated with the obtained results and the control proposal performance against disturbances is validated.

Suggested Citation

  • Flavio Muñoz & Ramon Garcia-Hernandez & Jose Ruelas & Juan E. Palomares-Ruiz & Carlos Álvarez-Macías, 2022. "Optimal Operation for Reduced Energy Consumption of an Air Conditioning System Using Neural Inverse Optimal Control," Mathematics, MDPI, vol. 10(5), pages 1-15, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:695-:d:756610
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    References listed on IDEAS

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    1. Chen, Wenjing & Chan, Ming-yin & Weng, Wenbing & Yan, Huaxia & Deng, Shiming, 2018. "An experimental study on the operational characteristics of a direct expansion based enhanced dehumidification air conditioning system," Applied Energy, Elsevier, vol. 225(C), pages 922-933.
    2. Alberto Garces-Jimenez & Jose-Manuel Gomez-Pulido & Nuria Gallego-Salvador & Alvaro-Jose Garcia-Tejedor, 2021. "Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study," Mathematics, MDPI, vol. 9(18), pages 1-24, September.
    3. Li, Ning & Xia, Liang & Shiming, Deng & Xu, Xiangguo & Chan, Ming-Yin, 2012. "Dynamic modeling and control of a direct expansion air conditioning system using artificial neural network," Applied Energy, Elsevier, vol. 91(1), pages 290-300.
    4. Akinkunmi Adegbenro & Michael Short & Claudio Angione, 2021. "An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications," Energies, MDPI, vol. 14(8), pages 1-18, April.
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    Cited by:

    1. Alma Y. Alanis, 2022. "Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications," Mathematics, MDPI, vol. 10(13), pages 1-2, July.

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